SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 1726–1750 of 3874 papers

TitleStatusHype
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data—0
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction—0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report—0
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: ReportCode1
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact RemovalCode2
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Temporal Consistency Learning of inter-frames for Video Super-ResolutionCode0
VIINTER: View Interpolation with Implicit Neural Representations of Images—0
BUbble Flow Field: a Simulation Framework for Evaluating Ultrasound Localization Microscopy Algorithms—0
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
TITAN: Bringing The Deep Image Prior to Implicit RepresentationsCode0
Self-supervised Character-to-Character Distillation for Text RecognitionCode1
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical SystemsCode0
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Data-Driven Computational Imaging for Scientific DiscoveryCode0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified